FCMPSO: An Imputation for Missing Data Features in Heart Disease Classification

Salleh, Mohd Najib Mohd; Samat, Nurul Ashikin

doi:10.1088/1757-899x/226/1/012102

Cited by 15 publications

(13 citation statements)

References 18 publications

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“…Missing data is a general weakness that can influence the consequences of the prediction system to be ineffective [1][2][3]. Ignoring the missing data has an impact on the results of the analysis [4][5][6][7][8][9], learning outcomes, predictive results [10] and potentially weakens the validity of the results and conclusions [8,9] and leads to estimation of biased parameters [7,[11][12][13][14]. Prediction and classification are the principle obligations required in many areas and spaces that expect admittance to finish and accurate data [15].…”

Section: Introductionmentioning

confidence: 99%

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

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Estimating missing data in a dataset is a significant advance during the data cleaning stage. Improper data handling can make inaccurate results when conducting data analysis. Most of the research about missing data estimation is irrespective of the correlation between attributes. However, an adaptive search procedure helps find the estimates of the missing data when correlations between attributes are considered in the process. Firefly Algorithm (FA) implements an adaptive search procedure in the imputation of the missing data by finding the estimated value that is closest to the value in other data known. Therefore, this study proposes a class center-based adaptive approach model for missing data by considering the attribute correlation in the imputation process (C3-FA). Based on the experiment, the general result find that the class center-based firefly algorithm is an efficient technique for getting the actual value in handling the missing data. This can be seen on the value of Pearson correlation coefficient (r) that close to 1 and the root mean squared error (RMSE) value is generally closer to 0. In addition, the proposed method can maintain the true distribution of data values. This is indicated by the Kolmogorov–Smirnov test that value of DKS for most of the attributes in the dataset is generally closer to 0. Also, the results of the accuracy evaluation using three classifiers, showed that the proposed method produces good accuracy.

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Section: Introductionmentioning

confidence: 99%

Class Center-Based Firefly Algorithm for Handling Missing Data

Nugroho

Utama

Surendro

2020

Preprint

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“…There are many imputation methods proposed specifically for microarray datasets. A number of effective imputations that have been used are clustering (Salleh & Samat, 2017) and classification algorithms (Tsai et al, 2018). Most articles proposed cluster-based algorithms and utilised high dimensional microarray datasets with a large number of features and samples that might directly affect the clustering performance (Keerin et al, 2016;Chattopadhyay et al, 2015;Gupta et al, 2015;Keerin et al, 2012) Moreover, the clustering performance is highly dependent on the number of clusters and with such conditions of samples, the selection of clusters will be crucial.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Missing Values Using Optimised Hybrid Fuzzy C-Means and Majority Vote for Microarray Data

Kumaran

Othman

Yusuf

2020

Journal of Information and Communication Technology

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Missing values are a huge constraint in microarray technologies towards improving and identifying disease-causing genes. Estimating missing values is an undeniable scenario faced by field experts. The imputation method is an effective way to impute the proper values to proceed with the next process in microarray technology. Missing value imputation methods may increase the classification accuracy. Although these methods might predict the values, classification accuracy rates prove the ability of the methods to identify the missing values in gene expression data. In this study, a novel method, Optimised Hybrid of Fuzzy C-Means and Majority Vote (opt-FCMMV), was proposed to identify the missing values in the data. Using the Majority Vote (MV) and optimisation through Particle Swarm Optimisation (PSO), this study predicted missing values in the data to form more informative and solid data. In order to verify the effectiveness of opt-FCMMV, several experiments were carried out on two publicly available microarray datasets (i.e. Ovary and Lung Cancer) under three missing value mechanisms with five different percentage values in the biomedical domain using Support Vector Machine (SVM) classifier. The experimental results showed that the proposed method functioned efficiently by showcasing the highest accuracy rate as compared to the one without imputations, with imputation by Fuzzy C-Means (FCM), and imputation by Fuzzy C-Means with Majority Vote (FCMMV). For example, the accuracy rates for Ovary Cancer data with 5% missing values were 64.0% for no imputation, 81.8% (FCM), 90.0% (FCMMV), and 93.7% (opt-FCMMV). Such an outcome indicates that the opt-FCMMV may also be applied in different domains in order to prepare the dataset for various data mining tasks.

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“…If the data gathered is not completed, issues may occur in the decision-making process. An incomplete dataset may also affect data mining models' performance, resulting in a lack of computing process efficiency and an invalid and inefficient outcome due to dataset gaps (Salleh & Samat, 2017). The main challenge of mining datasets is the existence of missing values (Poolsawad et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Advanced methods for missing values imputation based on similarity learning

Fouad

Ismail

Azar

et al. 2021

PeerJ Computer Science

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The real-world data analysis and processing using data mining techniques often are facing observations that contain missing values. The main challenge of mining datasets is the existence of missing values. The missing values in a dataset should be imputed using the imputation method to improve the data mining methods’ accuracy and performance. There are existing techniques that use k-nearest neighbors algorithm for imputing the missing values but determining the appropriate k value can be a challenging task. There are other existing imputation techniques that are based on hard clustering algorithms. When records are not well-separated, as in the case of missing data, hard clustering provides a poor description tool in many cases. In general, the imputation depending on similar records is more accurate than the imputation depending on the entire dataset's records. Improving the similarity among records can result in improving the imputation performance. This paper proposes two numerical missing data imputation methods. A hybrid missing data imputation method is initially proposed, called KI, that incorporates k-nearest neighbors and iterative imputation algorithms. The best set of nearest neighbors for each missing record is discovered through the records similarity by using the k-nearest neighbors algorithm (kNN). To improve the similarity, a suitable k value is estimated automatically for the kNN. The iterative imputation method is then used to impute the missing values of the incomplete records by using the global correlation structure among the selected records. An enhanced hybrid missing data imputation method is then proposed, called FCKI, which is an extension of KI. It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation algorithms to impute the missing data in a dataset. The fuzzy c-means algorithm is selected because the records can belong to multiple clusters at the same time. This can lead to further improvement for similarity. FCKI searches a cluster, instead of the whole dataset, to find the best k-nearest neighbors. It applies two levels of similarity to achieve a higher imputation accuracy. The performance of the proposed imputation techniques is assessed by using fifteen datasets with variant missing ratios for three types of missing data; MCAR, MAR, MNAR. These different missing data types are generated in this work. The datasets with different sizes are used in this paper to validate the model. Therefore, proposed imputation techniques are compared with other missing data imputation methods by means of three measures; the root mean square error (RMSE), the normalized root mean square error (NRMSE), and the mean absolute error (MAE). The results show that the proposed methods achieve better imputation accuracy and require significantly less time than other missing data imputation methods.

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FCMPSO: An Imputation for Missing Data Features in Heart Disease Classification

Cited by 15 publications

References 18 publications

Class Center-Based Firefly Algorithm for Handling Missing Data

Class Center-Based Firefly Algorithm for Handling Missing Data

Estimation of Missing Values Using Optimised Hybrid Fuzzy C-Means and Majority Vote for Microarray Data

Advanced methods for missing values imputation based on similarity learning

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